This invention relates generally to digital image processing, and more particularly the invention relates to edge detection in an optical image using a neural network classifier for pixel weighting.
Edge detection and enhancement is one of the most demanding tasks in optical image processing for artificial vision and image matching works. The edge detection process simplifies the subsequent analysis of images by drastically reducing the amount of data to be processed, while still preserving useful information about the image. Several approaches have been developed for edge detection. Among them, one may mention the Gaussian filters, which are the basis for a series of algorithms for detecting sharp edges. Several other methods for the detection of straight edges are based on producing a set of likely edge points by first applying some edge detection schemes, and then combining the resulting data to estimate the line coordinates of an edge; this can be done either by least squares fitting or by the Hough transform. There is also a projection-based detection method for straight line edges that analyzes the peaks in projection space to estimate the parameters of a line representing an edge.
In several applications, however, the assumption that the edges can be represented by sharp discontinuities is a poor one. Microlithography and wafer pattern analyzing and matching in IC-processing is one of these cases, where the edge profiles are smoothed out and blurred and the corners are rounded off by both process-introduced defects such as imperfect etching, and by filtering effects and aberrations introduced by the optical imaging system. Furthermore, the optical images taken from the wafer contain noise due to several sources such as random local changes in reflectivity of the wafer surface and the noise introduced by the imaging system. In this environment, some useful results have been obtained by Douglas and Meng, who proposed to use a neural classifier element to recognize the position of an edge by classifying the pixels into edge/non-edge categories. They use a modified sigmoid-LMS algorithm and in order to teach the filter (or adapt the weights), they artificially generate a raster-scan image in which the edges arrive as the result of a Markov finite state process, and then they add Gaussian noise to the resulting image. In this manner, their filter weights are adapted through comparing the filter output and the assumed desired response.